
%matplotlib inline

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import cm

import ipyparallel as ipp

from time import time
from datetime import datetime

import motif as mf

from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.decomposition import PCA
from sklearn.utils import shuffle
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import roc_curve, roc_auc_score
from sklearn.model_selection import train_test_split, cross_val_score, cross_validate, RepeatedKFold

from scipy.stats import spearmanr
from scipy.stats import pearsonr

Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)


### set parameters for the motif analysis

PROTEIN_NAME='T7 primase'
PROT_CONC=5 # free protein concentration at binding reation; PBM typically 0.1 and RNACompete typically 0.002
BOTH_STRANDS=False # wheter both strands are present for binding; True if double-stranded DNA or RNA is used as probes

STAGES=mf.stage(protein=PROTEIN_NAME)


### read data

dfprobes_raw=pd.read_csv('./data/T7/chip_B_favor.csv', sep=';')
print('Columns of imported Data File: %s'%dfprobes_raw.columns)

Columns of imported Data File: Index(['0_nuc', '1_nuc', '2_nuc', '3_nuc', '4_nuc', '5_nuc', '6_nuc', '7_nuc',
       '8_nuc', '9_nuc', '10_nuc', '11_nuc', '12_nuc', '13_nuc', '14_nuc',
       '15_nuc', '16_nuc', '17_nuc', '18_nuc', '19_nuc', '20_nuc', '21_nuc',
       '22_nuc', '23_nuc', '24_nuc', '25_nuc', '26_nuc', '27_nuc', '28_nuc',
       '29_nuc', '30_nuc', '31_nuc', '32_nuc', '33_nuc', '34_nuc', '35_nuc',
       'prim_only_score', 'prim', 'poly', 'seq'],
      dtype='object')


### select columns for probe sequence and signal

column_sequence='seq'
column_signal='prim_only_score'
#background_signal='mean_background_intensity'
background_signal=None                             #set to None if not needed

#basic preprocessing
dfprobes_raw[column_signal]=dfprobes_raw[column_signal].apply(lambda a: np.NaN if a==' ' else a)
dfprobes_raw[column_sequence]=dfprobes_raw[column_sequence].apply(lambda a: np.NaN if a=='nan' else a)
dfprobes_raw=dfprobes_raw.dropna()

#construct new dataframe with only necessary data
if type(background_signal)==type(None):
    dfprobes=pd.DataFrame({'seq':dfprobes_raw[column_sequence].astype(str),
                           'signal binding': dfprobes_raw[column_signal].astype(np.float32)}) #rebuild dataframe
else:
    dfprobes=pd.DataFrame({'seq':dfprobes_raw[column_sequence].astype(str),
                           'signal': dfprobes_raw[column_signal].astype(np.float32), 
                           'background':dfprobes_raw[background_signal].astype(np.float32)}) #rebuild dataframe
    dfprobes['signal binding']=dfprobes['signal']-dfprobes['background']
# display main properties of data set

dfprobes['signal binding']=dfprobes['signal binding']**2  # as raw data was squared

dfprobes['signal binding'].plot(figsize=(15,5))
dfprobes.describe()

### check type of nucleic acid

dfprobes['seq']=dfprobes['seq'].apply(lambda seq: seq.upper().replace(" ", ""))  #upper and remove blanks
dfprobes['RNA']=dfprobes['seq'].apply(lambda seq: all(char in 'ACGU' for char in seq))
dfprobes['DNA']=dfprobes['seq'].apply(lambda seq: all(char in 'ACGT' for char in seq))
non_RNA_counts=len(dfprobes[dfprobes['RNA']==False])
non_DNA_counts=len(dfprobes[dfprobes['DNA']==False])

if non_RNA_counts<non_DNA_counts:
    NUC_TYPE='RNA'; print('I: RNA probes detected!')
else: NUC_TYPE='DNA'; print('I: DNA probes detected!')
    
if NUC_TYPE=='RNA' and non_RNA_counts!=0:
    print('E: The probe sequences appear to be RNA, however there are some non-RNA nucleotides in the sequences.')
    print('E: Please check the following sequnces %s'%dfprobes[dfprobes['RNA']==False])

if NUC_TYPE=='DNA' and non_DNA_counts!=0:
    print('E: The probe sequences appear to be RNA, however there are some non-RNA nucleotides in the sequences.')
    print('E: Please check the following sequnces %s'%dfprobes[dfprobes['DNA']==False])

I: DNA probes detected!


### option to add a constant sequence at the 3' end and 5' end
sequence_to_be_added_5='' 
sequence_to_be_added_3=''        # standard PBM arrays: CCTGTGTGAAATTGTTATCCGCTCT T7 array: GTCTTGA..
dfprobes['seq']=sequence_to_be_added_5.upper()+dfprobes['seq']+sequence_to_be_added_3.upper()
print(f"I: The nucleotide sequences {sequence_to_be_added_5.upper()} has been added to the 5' end all probe sequences.")
print(f"I: The nucleotide sequences {sequence_to_be_added_3.upper()} has been added to the 3' end all probe sequences.")

I: The nucleotide sequences  has been added to the 5' end all probe sequences.
I: The nucleotide sequences  has been added to the 3' end all probe sequences.


### egalize length
dfprobes['seq_length']=dfprobes['seq'].apply(len)    

if max(dfprobes['seq_length'])!=min(dfprobes['seq_length']):
    print('I: Probes length is not uniform, detected range: %i ..%i' %(min(dfprobes['seq_length']),max(dfprobes['seq_length'])))
    max_length=max(dfprobes['seq_length'])
    dfprobes['padded_sequence']=dfprobes['seq'].apply(lambda seq: seq+((max_length-len(seq))*'-'))
    print("I: Probe sequences have been padded at the 5' to the uniform length of %i nucleotides" %max_length)
else:
    print('I: Probe sequences have the uniform length of %i nucleotides' %(dfprobes['seq_length'].median()))
    dfprobes['padded_sequence']=dfprobes['seq']

print('I: Total datasets contains %i sequences.' % len(dfprobes))

# visualize composition of each position
df_nucleotides=mf.split_sequence_in_nucleotides(dfprobes['padded_sequence'])
dfcount=pd.DataFrame(index=['A', 'C', 'G', 'T', 'U', '-'])
for column in df_nucleotides:
    dfcount[column]=df_nucleotides[column].value_counts() 
dfcount=dfcount.fillna(0) #zeros for NaN
dfcount.transpose().plot(figsize=(15,5), kind='bar')
print('I: Visualisation of the base composition per position')
print('I: If positions are invariant they can be removed before sequence analysis.')

I: Probe sequences have the uniform length of 36 nucleotides
I: Total datasets contains 3149 sequences.
I: Visualisation of the base composition per position
I: If positions are invariant they can be removed before sequence analysis.


# You may remove invariant continuos positions by adjusting the slicing. 
# It is recommended to leave a few invariant positions to allow for binding events
# between the variable and constant part of the probes.

dfprobes['padded_sequence']=dfprobes['padded_sequence'].apply(lambda s:s[:43]) ### <==== do the slicing here

# visualize composition of each position
print('I: Visualisation of the base composition per position after slicing.')
df_nucleotides=mf.split_sequence_in_nucleotides(dfprobes['padded_sequence'])
dfcount=pd.DataFrame(index=['A', 'C', 'G', 'T', 'U', '-'])
for column in df_nucleotides:
    dfcount[column]=df_nucleotides[column].value_counts() 
dfcount=dfcount.fillna(0) #zeros for NaN
dfcount.transpose().plot(figsize=(15,5), kind='bar')
plt.show()

### Preparation for later classification
mean=dfprobes['signal binding'].mean()
std=dfprobes['signal binding'].std()
THRESHOLD=mean+2*std   
dfprobes['positive probe']=dfprobes['signal binding'].apply(lambda s: True if s>THRESHOLD else False)

print('I: The whole dataset has been used to set the threshold for a positive probe.')
print('T: The threshold is %f' %THRESHOLD)
print(f"I: {len(dfprobes[dfprobes['positive probe']])} probes of {len(dfprobes)} are above threshold.")

I: Visualisation of the base composition per position after slicing.

I: The whole dataset has been used to set the threshold for a positive probe.
T: The threshold is 0.663948
I: 196 probes of 3149 are above threshold.


### Shuffle and prepare dataset for training and testing

# shuffle 
dfprobes = shuffle(dfprobes)

# display histogramms of dataset
dfprobes['signal binding'].plot(kind='hist', bins=25).axvline(x=THRESHOLD, color='r', linestyle='-.', lw=0.5, label='threshold classification')

# complete data
X=mf.hotencode_sequence(dfprobes['padded_sequence'], nuc_type=NUC_TYPE)
y=np.array(dfprobes['signal binding'])


#### Perfrom GridCV Search for exploration of the motif length and other model parameters 
# primary goal: identify the minimum motif length which gives a good r-value
# optional: allow also for global optimization to verify whether the local optimization is good enough
# optional: vary G0 to find a good regression model
# optional: fitG0=True. This option adjust G0, so that maximal binding is around 1 (prevents saturation of probes by multiple binding events)


model_grid=mf.findmotif(protein_conc=PROT_CONC, both_strands=BOTH_STRANDS, fit_G0=True)

# prepare grid search over parameters
param_grid = {"motif_length": [1,2,3,4,5,6]}     # choose sensible range for length of motif and other parameters

# run grid search
grid_search = GridSearchCV(model_grid, param_grid=param_grid, verbose=2, 
                           cv=RepeatedKFold(n_splits=5, n_repeats=10),
                           n_jobs=-1,
                           return_train_score=True)

start = time()
grid_search.fit(X, y)
df_grid=pd.DataFrame(grid_search.cv_results_)

print("I: GridSearchCV took %.2f hours for %d candidate parameter settings."
    % ((time() - start)/3600, len(grid_search.cv_results_["params"])))
print('I: number of samples: %i' %len(X))

df_grid=pd.DataFrame(grid_search.cv_results_)

print('I: Plot of r vs motif length')
df_grid['r(train)']=df_grid['mean_train_score'].apply(np.sqrt)
df_grid['r(test)']=df_grid['mean_test_score'].apply(np.sqrt)
df_grid['std(train)']=df_grid['std_train_score'].apply(np.sqrt)
df_grid['std(test)']=df_grid['std_test_score'].apply(np.sqrt)


fig, ax=plt.subplots()
df_grid.plot(ax=ax,kind='scatter', x='param_motif_length', y='r(train)', yerr='std(train)', figsize=(5,3), c='blue').set_xticks(param_grid["motif_length"])
df_grid.plot(ax=ax,kind='scatter', x='param_motif_length', y='r(test)', yerr='std(test)', c='red')
ax.set_ylabel('r')
ax.legend(['train', 'test'])
plt.show()
df_grid[['param_motif_length','r(test)', 'r(train)']]

Fitting 50 folds for each of 6 candidates, totalling 300 fits
I: GridSearchCV took 12.73 hours for 6 candidate parameter settings.
I: number of samples: 3149
I: Plot of r vs motif length


%store df_grid

Stored 'df_grid' (DataFrame)


%store param_grid

Stored 'param_grid' (dict)


### run a number of identical optimizations with motif length found during grid search
### goal: find best motif through repetition, judge stabiltiy of optimization

CORE_MOTIF_LENGTH=4  # adjust core motif length if needed, motif length can be changed later

# prepare for ipyparallel
number_of_optimizations = 200
model_list = [mf.findmotif(motif_length=CORE_MOTIF_LENGTH, protein_conc=PROT_CONC, both_strands=BOTH_STRANDS, fit_G0=True)] * number_of_optimizations
X_list = [X] * number_of_optimizations
y_list = [y] * number_of_optimizations


def single_job(model, X, y):
    model.fit(X, y)
    return {'model':model}

# run the optimizations on ipp.cluster
start = time()
with ipp.Cluster(log_level=40) as rc:
    rc[:].use_pickle()
    view = rc.load_balanced_view()
    asyncresult = view.map_async(single_job, model_list, X_list, y_list)
    asyncresult.wait_interactive()
    result = asyncresult.get()
print("I: Optimization took %.2f hours." % ((time() - start) / 3600))


  
# assemble results and analyze
df_repetitions=pd.DataFrame(result)
df_repetitions['r']=df_repetitions['model'].apply(lambda e: mf.linregress(e.predict(X),y).rvalue)
df_repetitions['G0']=df_repetitions['model'].apply(lambda e: e.finalG0_)
df_repetitions['max binding']=df_repetitions['model'].apply(lambda e: e.max_binding_fit)
df_repetitions['min binding']=df_repetitions['model'].apply(lambda e: e.min_binding_fit)
df_repetitions['ratio'] = df_repetitions['model'].apply(lambda e: e.ratio)
df_repetitions['energies']=df_repetitions['model'].apply(lambda e: e.energies_)
df_repetitions['information']=df_repetitions['model'].apply(lambda e: mf.energies2information(e.energies_))


# display results of the ensemble of optimizations
print('I: Results of the repeated motif finding, sorted according to the regression coefficient')
df_repetitions.sort_values('r', ascending=False, inplace=True)
mf.display_df(df_repetitions, nuc_type=NUC_TYPE)

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I: Optimization took 9.36 hours.
I: Results of the repeated motif finding, sorted according to the regression coefficient


df_repetitions[:-int(len(df_repetitions)/20)] #delete 5% worst


df_repetitions=df_repetitions[:-int(len(df_repetitions)/20)] #delete 5% worst


### compare energy matrices of ensemble using PCA
print('I: Histogram of the regression coefficients r obtained by repeated optimizaion with the subset.')
df_repetitions['r'].plot(kind='hist')
plt.show()

pca = PCA(n_components=2)
pca_2c=pca.fit_transform(df_repetitions['energies'].tolist())    
df_repetitions[['PCA1', 'PCA2']]=pca_2c
print('I: 2-dimensional PCA explained %i %% of variance.' %(sum(pca.explained_variance_ratio_)*100))
if sum(pca.explained_variance_ratio_)<0.5:
      print('W: 2-dimensional PCA explained only %i %% of variance' %(sum(pca.explained_variance_ratio_)*100))

print('I: Visualization of the PCA with the regression quality by color.')        
df_repetitions.plot(x='PCA1', y='PCA2', kind='scatter', c='r',cmap=cm.coolwarm, edgecolors='black', linewidths=0.3)

I: Histogram of the regression coefficients r obtained by repeated optimizaion with the subset.

I: 2-dimensional PCA explained 79 % of variance.
I: Visualization of the PCA with the regression quality by color.

/home/GLipps/.local/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute `n_features_` was deprecated in version 1.2 and will be removed in 1.4. Use `n_features_in_` instead.
  warnings.warn(msg, category=FutureWarning)

<matplotlib.axes._subplots.AxesSubplot at 0x7fe661562a30>


pca.explained_variance_ratio_

array([0.62422327, 0.17305216])


fig=df_repetitions.plot(x='PCA1', y='PCA2', kind='scatter', c='r',cmap=cm.coolwarm, edgecolors='black', 
                        linewidths=0.3).get_figure()
fig.savefig('PCA_no_padding.svg')


mf.display_df(df_repetitions, nuc_type=NUC_TYPE)


# visualisation of the motif with the highest r
print('I: Best motif according to r from the repeated optimizations.')
print('I: PCA components: %i, %i' %(df_repetitions.iloc[0]['PCA1'], df_repetitions.iloc[0]['PCA2']))
model_best_repetition=df_repetitions.iloc[0]['model']
model_best_repetition.analyse_motif(X,y, THRESHOLD, nuc_type=NUC_TYPE) 
# store results and display stages
STAGES.append('best repetition', model_best_repetition)
mf.display_df(STAGES.df, nuc_type=NUC_TYPE)

I: Best motif according to r from the repeated optimizations.
I: PCA components: 19825, -1553
I: energy matrix and logos:

       A      C     G     T
0 -7053  14371 -8323  1004
1  5640  -4676  5637 -6602
2   349  -2266  2198  -281
3  -790   1047   965 -1221

I: summed absolute energies of each position:
0    30752
1    22557
2     5096
3     4024
dtype: int64

I: averaged summed energy over all positions: 15607
I: Mean and Standard Deviation for the Free Energy G to all subsequences of all probes: -9109 +/- 9135
I: Plot of the Occupancy of a subsite as the function of the Free Energy G 
   overlaid with the distribution of the Free Energy of all subsites.
I: There shall be only a small overlap of both curves. i.e. only the most negative Free Energies
    lead to a measurable occupancy.

I: Calculated occupancy over all subsite of a single probe:
   binding:  0.13331 .. 0.97203 (ratio: 7.3)
I: number of probes: 3149
I: Pearson Correlation  r: 0.7070
I: mean absolute error: 0.1028

I: Classification performance AUROC: 0.8295


# refit model to minimize mae
last_model=STAGES.df.at[max(STAGES.df.index),'model']
refitted_model_best_repetition=model_best_repetition.refit_mae(X,y)

# print & display main results
refitted_model_best_repetition.analyse_motif(X,y, THRESHOLD, nuc_type=NUC_TYPE)

# store results and display stages
STAGES.append('mae optimzed', refitted_model_best_repetition)
mf.display_df(STAGES.df, nuc_type=NUC_TYPE)

Optimization terminated successfully.
         Current function value: 0.098459
         Iterations: 8
         Function evaluations: 1734
I: energy matrix and logos:

       A      C     G     T
0 -6638  15358 -8211  -508
1  5720  -4656  5614 -6678
2    35  -2089  2203  -149
3  -482   1071   713 -1302

I: summed absolute energies of each position:
0    30716
1    22669
2     4478
3     3569
dtype: int64

I: averaged summed energy over all positions: 15358
I: Mean and Standard Deviation for the Free Energy G to all subsequences of all probes: -9090 +/- 9549
I: Plot of the Occupancy of a subsite as the function of the Free Energy G 
   overlaid with the distribution of the Free Energy of all subsites.
I: There shall be only a small overlap of both curves. i.e. only the most negative Free Energies
    lead to a measurable occupancy.

I: Calculated occupancy over all subsite of a single probe:
   binding:  0.13499 .. 0.97839 (ratio: 7.3)
I: number of probes: 3149
I: Pearson Correlation  r: 0.6931
I: mean absolute error: 0.0985

I: Classification performance AUROC: 0.8204


### Based on the motif of CORE_MOTIF_LENGTH analyze the neigbouring positions 
### whether their inclusion can improve the quality of the motif
df_positions=model_best_repetition.investigate_extension_parallel(X, y, end5=3, end3=3, nuc_type=NUC_TYPE)

list_positions=df_positions.index[df_positions['+2%']].tolist()+[0] # list of positions with an increase of2% and default position 0
ext5=-min(list_positions)
ext3=max(list_positions)
print("I: It is suggested to extend the core motif at the 5' end by %i and at the 3' end by %i positions." %(ext5, ext3))

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I: Optimization took 0.02 hours.

I: It is suggested to extend the core motif at the 5' end by 0 and at the 3' end by 0 positions.


### fit & predict optimization starting with extended energy matrix if extension appears to improve prediction

if ext5+ext3!=0: #extension suggestion from previous analysis of the bordering positions
    expanded_energies=model_best_repetition.energies_
    # append energies of single-optimized bordering positions to energies of central part
    if ext5!=0:
        energies_5=np.concatenate(df_positions['energies'][(df_positions.index<0) & (df_positions.index>=-ext5)].to_numpy())
        expanded_energies=np.concatenate((energies_5, expanded_energies))
    if ext3!=0:
        energies_3=np.concatenate(df_positions['energies'][(df_positions.index<=ext3) & (df_positions.index>0)].to_numpy().flatten())
        expanded_energies=np.concatenate((expanded_energies,  energies_3))

    mf.energies2logo(expanded_energies, nuc_type=NUC_TYPE)
    print('I: Optimization started with following extended motif.')
    expanded_motif_length=len(expanded_energies)//4
    
    
    model_extended=mf.findmotif(motif_length=expanded_motif_length, protein_conc=PROT_CONC, both_strands=BOTH_STRANDS,
                   start=expanded_energies, fit_G0=True)

    start = time()
    model_extended.fit(X,y)
    print("Optimization took %.2f hours." % ((time() - start)/3600))

    # print & display main results
    model_extended.analyse_motif(X,y, THRESHOLD, nuc_type=NUC_TYPE)

    # store results and display stages
    STAGES.append('extended', model_extended)
    mf.display_df(STAGES.df, nuc_type=NUC_TYPE)
else:
    print('I: Motif is not extended based on previous analysis.')

I: Motif is not extended based on previous analysis.


### fit & predict optimization starting with extended energy matrix plus one bordering position on each side if current bordering position exceed the information of 0.25

last_model=STAGES.df.at[max(STAGES.df.index),'model']
I_5=mf.energies2information(last_model.energies_[0:4])>=0.25 #sufficient information content of 5' end position
I_3=mf.energies2information(last_model.energies_[-4:])>=0.25 #sufficient information content of 3' end position

if I_5 or I_3:
    print('I: At least one of the bordering positions of the current motif has an information content of at least 0.25. Extending.')
    expanded_energies_with_border=mf.modify_energies(last_model.energies_, end5=I_5, end3=I_3)  
    mf.energies2logo(expanded_energies_with_border, nuc_type=NUC_TYPE)
    motif_length_with_border=len(expanded_energies_with_border)//4

    model_with_border=mf.findmotif(motif_length=motif_length_with_border, protein_conc=PROT_CONC, both_strands=BOTH_STRANDS,
                   start=expanded_energies_with_border, fit_G0=True)


    start = time()
    model_with_border.fit(X,y)
    print("Optimization took %.2f hours." % ((time() - start)/3600))

    # print & display main results
    model_with_border.analyse_motif(X,y, THRESHOLD, nuc_type=NUC_TYPE)

    # store results and display stages
    STAGES.append('expanded, border', model_with_border)
    mf.display_df(STAGES.df, nuc_type=NUC_TYPE)
else:
    print('I: Both bordering positions of the found motif have an information content below 0.25. No futher optimization required.')

I: At least one of the bordering positions of the current motif has an information content of at least 0.25. Extending.

Optimization took 0.23 hours.
I: energy matrix and logos:

       A     C     G     T
0  1225  -591  -397  -236
1 -7152  9225 -8511  6438
2  5548 -4640  5593 -6501
3   428 -2416  2263  -275
4  -937   900  1026  -989

I: summed absolute energies of each position:
0     2451
1    31326
2    22284
3     5383
4     3854
dtype: int64

I: averaged summed energy over all positions: 13060
I: Mean and Standard Deviation for the Free Energy G to all subsequences of all probes: -8708 +/- 7835
I: Plot of the Occupancy of a subsite as the function of the Free Energy G 
   overlaid with the distribution of the Free Energy of all subsites.
I: There shall be only a small overlap of both curves. i.e. only the most negative Free Energies
    lead to a measurable occupancy.

I: Calculated occupancy over all subsite of a single probe:
   binding:  0.07823 .. 0.97513 (ratio: 12.5)
I: number of probes: 3149
I: Pearson Correlation  r: 0.7229
I: mean absolute error: 0.1000

I: Classification performance AUROC: 0.8395


last_model=STAGES.df.at[max(STAGES.df.index),'model']
df_relevant_positions=last_model.explore_positions(X, y)
list_positions=df_relevant_positions.index[df_relevant_positions['-2%']].tolist() # list of positions with an increase of2% and default position 0
start_relevant=min(list_positions)
end_relevant=max(list_positions)
red5=-start_relevant
red3=end_relevant-len(df_relevant_positions)+1
print('I: The analysis suggests, that positions between %i to %i contribute significantly to the motif' %(start_relevant, end_relevant))
last_model=STAGES.df.at[max(STAGES.df.index),'model']

if (end_relevant-start_relevant+1)in STAGES.df['motif length'].to_list():
    print('I: No need for a further optimization. An optimization with motif length of %i has already been done.' %(end_relevant-start_relevant+1))
    print('I: Checking whether G0 has been chosen correctly.')
    last_model.investigate_G0(X, y)
else:
    print('I: Bordering positions only marginally contributing towards regression quality are dropped.')
    print('I: New start energy for motif optimization:')
    start_final_model=mf.modify_energies(last_model.energies_, end5=red5, end3=red3)
    mf.energies2logo(start_final_model, nuc_type=NUC_TYPE)
    final_model=mf.findmotif(motif_length=len(start_final_model)//4, protein_conc=PROT_CONC, 
                             both_strands=BOTH_STRANDS, start=start_final_model, fit_G0=True)

    start = time()
    final_model.fit(X,y)
    print("Optimization took %.2f hours." % ((time() - start)/3600))

    # print & display main results
    final_model.analyse_motif(X,y, THRESHOLD, nuc_type=NUC_TYPE)
    
    print('I: Checking whether G0 has been chosen correctly.')
    final_model.investigate_G0(X, y)

    # store results and display stages
    STAGES.append('shrinked', final_model)
    mf.display_df(STAGES.df, nuc_type=NUC_TYPE)

I: The analysis suggests, that positions between 0 to 4 contribute significantly to the motif
I: No need for a further optimization. An optimization with motif length of 5 has already been done.
I: Checking whether G0 has been chosen correctly.
I: Current G0 = -8721 J/mol (see red broken line in figure below) with r = 0.723.
I: Maximal r is 0.724 at G0=-6721 J/mol (see green broken line below).
I: Maximal occupancy of 2 is reached at G0=-10721 J/mol (see blue broken line below).
I: Maximal occupancy of 0.2 is reached at G0=-4721 J/mol (see blue broken line below).

I: G0 is in a range leading to maximal probe occupancy between 0.2 and 2. Good.
I: Maximal r is close to r achieved with current G0. Good.


# refit model to minimize mae
last_model=STAGES.df.at[max(STAGES.df.index),'model']
refitted_model=last_model.refit_mae(X,y)

# print & display main results
refitted_model.analyse_motif(X,y, THRESHOLD, nuc_type=NUC_TYPE)

# store results and display stages
STAGES.append('final mae optimzed', refitted_model)
mf.display_df(STAGES.df, nuc_type=NUC_TYPE)

Optimization terminated successfully.
         Current function value: 0.096293
         Iterations: 11
         Function evaluations: 2870
I: energy matrix and logos:

       A      C     G     T
0  1245   -457  -285  -502
1 -6968  12092 -8413  3288
2  5629  -4538  5498 -6588
3  -240  -2118  2580  -221
4  -608    943   840 -1175

I: summed absolute energies of each position:
0     2491
1    30762
2    22254
3     5161
4     3567
dtype: int64

I: averaged summed energy over all positions: 12847
I: Mean and Standard Deviation for the Free Energy G to all subsequences of all probes: -8674 +/- 8388
I: Plot of the Occupancy of a subsite as the function of the Free Energy G 
   overlaid with the distribution of the Free Energy of all subsites.
I: There shall be only a small overlap of both curves. i.e. only the most negative Free Energies
    lead to a measurable occupancy.

I: Calculated occupancy over all subsite of a single probe:
   binding:  0.07442 .. 0.88433 (ratio: 12.5)
I: number of probes: 3149
I: Pearson Correlation  r: 0.7096
I: mean absolute error: 0.0963

I: Classification performance AUROC: 0.8235


STAGES.df['energies'].apply(mf.energies2information)

0    2.362101
1    2.285369
2    2.500981
3    2.437509
Name: energies, dtype: float64


STAGES.df.to_json('%s_%s-%s-%s_%s-%s.json' %(PROTEIN_NAME, datetime.now().year, datetime.now().month,datetime.now().day , datetime.now().hour, datetime.now().minute))

	param_motif_length	r(test)	r(train)
0	1	0.177967	0.187764
1	2	0.663136	0.666127
2	3	0.676877	0.678723
3	4	0.669234	0.673278
4	5	0.697536	0.705088
5	6	0.696467	0.705967

	model	r	G0	max binding	min binding	ratio	energies	information
56	suppressed	0.706996	-9177.028204	0.972033	0.133311	7.291495	-7053,..	2.362101
142	suppressed	0.706961	-10863.349327	0.964528	0.124707	7.734337	-5334,..	2.310951
157	suppressed	0.706862	-9727.766114	0.966156	0.135750	7.117146	-6577,..	2.317627
148	suppressed	0.706826	-11675.698748	0.967140	0.134469	7.192312	-4909,..	2.269945
163	suppressed	0.706807	-11486.910299	0.965590	0.136809	7.057952	-5785,..	2.214978
131	suppressed	0.706774	-9728.752465	0.979635	0.121770	8.044950	-5290,..	2.300738
106	suppressed	0.706773	-7915.539991	0.952564	0.124481	7.652290	-8470,..	2.364914
147	suppressed	0.706685	-10933.515501	0.965363	0.132006	7.313014	-4346,..	2.340699
70	suppressed	0.706631	-12214.012249	0.963979	0.139102	6.930042	-5148,..	2.223107
89	suppressed	0.706580	-11248.116017	0.968648	0.136544	7.094042	-6221,..	2.123406
170	suppressed	0.706539	-12052.588484	0.963785	0.144152	6.685888	-4561,..	2.256433
113	suppressed	0.706530	-11913.812017	0.969498	0.126538	7.661721	-4487,..	2.247607
52	suppressed	0.706515	-12926.542010	0.965676	0.140475	6.874351	-4724,..	2.109999
2	suppressed	0.706428	-12557.874320	0.958903	0.147678	6.493219	-4832,..	2.246795
193	suppressed	0.706419	-13273.206683	0.974611	0.142844	6.822906	-4308,..	2.144290
67	suppressed	0.706398	-12923.995193	0.967148	0.137279	7.045115	-4610,..	2.111763
115	suppressed	0.706326	-12983.544326	0.972911	0.146713	6.631377	-5042,..	2.079642
59	suppressed	0.706294	-12176.505872	0.965304	0.148764	6.488806	-5943,..	2.064881
0	suppressed	0.706252	-12343.309172	0.956334	0.143811	6.649934	-5709,..	2.072961
117	suppressed	0.706111	-12177.537911	0.970942	0.134118	7.239444	-3974,..	2.095936
18	suppressed	0.706074	-13749.828576	0.975109	0.156092	6.246997	-4223,..	2.058359
127	suppressed	0.706036	-10854.233141	0.953125	0.141681	6.727261	-6960,..	2.116820
79	suppressed	0.705990	-12794.544096	0.974744	0.141340	6.896470	-4728,..	2.046540
27	suppressed	0.705884	-10317.128912	0.967302	0.150407	6.431250	-4349,..	2.238211
77	suppressed	0.705877	-14307.909056	0.969338	0.152928	6.338521	-4033,..	1.948135
5	suppressed	0.705833	-13465.399731	0.968937	0.154969	6.252474	-4878,..	1.966427
28	suppressed	0.705824	-13257.450651	0.970967	0.151509	6.408643	-5022,..	2.031888
12	suppressed	0.705811	-11814.471246	0.965943	0.154481	6.252820	-6645,..	2.010246
190	suppressed	0.705788	-14142.449874	0.973330	0.155344	6.265625	-4195,..	2.043055
130	suppressed	0.705769	-13728.165226	0.967874	0.140905	6.868995	-4015,..	2.022657
105	suppressed	0.705719	-14379.041850	0.978379	0.154051	6.350986	-4013,..	1.993526
111	suppressed	0.705703	-12947.902083	0.970576	0.149884	6.475512	-4823,..	1.953821
135	suppressed	0.705640	-14039.619117	0.973976	0.150254	6.482181	-3802,..	2.004695
43	suppressed	0.705622	-14487.299010	0.967699	0.160324	6.035891	-3927,..	1.978947
83	suppressed	0.705541	-14577.425748	0.981237	0.163550	5.999600	-4077,..	1.946875
178	suppressed	0.705485	-14061.491830	0.968489	0.159125	6.086355	-3858,..	1.959808
32	suppressed	0.705459	-14266.668919	0.967770	0.170622	5.672020	-4150,..	2.023246
26	suppressed	0.705413	-14701.042064	0.978571	0.169238	5.782209	-3986,..	1.912349
54	suppressed	0.705339	-14959.846587	0.975378	0.162206	6.013202	-3678,..	1.902153
45	suppressed	0.705324	-13361.090070	0.968532	0.159637	6.067098	-4836,..	2.026302
114	suppressed	0.705315	-12670.666987	0.976832	0.158935	6.146115	-6041,..	1.986389
24	suppressed	0.705314	-14054.154743	0.980018	0.155556	6.300092	-4528,..	1.938419
14	suppressed	0.705282	-14088.876587	0.974698	0.152134	6.406837	-3837,..	2.043591
21	suppressed	0.705188	-8305.931026	0.962993	0.144902	6.645806	-8188,..	2.340227
48	suppressed	0.705135	-14723.661981	0.972560	0.172590	5.635099	-3644,..	1.984678
36	suppressed	0.705020	-14820.934033	0.977018	0.168904	5.784454	-3864,..	1.823897
120	suppressed	0.704992	-14451.163255	0.978946	0.175247	5.586075	-4413,..	1.908539
140	suppressed	0.704955	-12370.971274	0.974654	0.156738	6.218381	-6621,..	1.886788
124	suppressed	0.704954	-11465.242231	0.967891	0.169187	5.720832	-7332,..	1.946722
139	suppressed	0.704909	-13699.534998	0.972513	0.166307	5.847711	-4736,..	2.000360
100	suppressed	0.704803	-15229.762285	0.991523	0.166159	5.967320	-3816,..	1.799674
187	suppressed	0.704578	-14778.630303	0.961413	0.175678	5.472591	-4233,..	1.857307
181	suppressed	0.704422	-15758.529236	0.994799	0.192793	5.159941	-3386,..	1.755134
3	suppressed	0.704033	-14230.229409	0.971167	0.173523	5.596750	-3634,..	1.964398
7	suppressed	0.704010	-13187.195178	0.972048	0.172629	5.630850	-5467,..	2.011791
25	suppressed	0.703962	-15155.136559	0.974104	0.186596	5.220385	-3974,..	1.817961
60	suppressed	0.703665	-15048.014390	1.002399	0.193495	5.180495	-4302,..	1.708500
46	suppressed	0.703537	-14556.597842	0.979293	0.175544	5.578615	-4895,..	1.762471
16	suppressed	0.703486	-15225.570281	0.971553	0.173898	5.586916	-3398,..	1.814056
123	suppressed	0.703194	-16545.540324	0.981143	0.209328	4.687109	-3038,..	1.554067
72	suppressed	0.703165	-15850.112081	0.973095	0.203243	4.787836	-3353,..	1.773192
75	suppressed	0.698884	-18233.733094	0.996786	0.311778	3.197099	-2342,..	1.082989
38	suppressed	0.695414	-13623.049624	0.994722	0.202557	4.910824	-3831,..	2.396506
138	suppressed	0.694687	-15089.902655	1.014728	0.220736	4.597027	-3338,..	2.119809
23	suppressed	0.694529	-16078.835412	1.013021	0.181807	5.571966	3802,..	1.433959
152	suppressed	0.694514	-15316.049402	0.999945	0.148745	6.722527	4993,..	1.550912
11	suppressed	0.694501	-16062.485560	0.998670	0.169212	5.901884	3614,..	1.456914
112	suppressed	0.694494	-13742.600462	0.997189	0.111924	8.909516	6957,..	1.689719
175	suppressed	0.694472	-14164.875513	0.996417	0.138673	7.185389	5902,..	1.575634
66	suppressed	0.694472	-15002.774532	1.008638	0.126429	7.977893	5820,..	1.604546
168	suppressed	0.694467	-13724.735721	1.004490	0.133346	7.532940	6287,..	1.566653
184	suppressed	0.694458	-15387.275370	0.994654	0.188431	5.278623	2995,..	1.401132
133	suppressed	0.694448	-12414.691250	0.994884	0.109906	9.052176	9312,..	1.711392
93	suppressed	0.694435	-14169.410972	1.002387	0.124961	8.021589	9148,..	1.639763
62	suppressed	0.694433	-12264.565203	1.005852	0.122552	8.207558	9222,..	1.686218
183	suppressed	0.694432	-14371.123342	0.994981	0.193133	5.151792	3347,..	1.392146
53	suppressed	0.694412	-15834.883136	1.005492	0.156837	6.411059	4776,..	1.484806
96	suppressed	0.694405	-15150.371077	1.000244	0.148052	6.756014	3849,..	1.513579
68	suppressed	0.694395	-13458.785667	0.997705	0.120960	8.248227	8730,..	1.687686
176	suppressed	0.694343	-14975.228364	1.005385	0.155619	6.460571	3759,..	1.504388
74	suppressed	0.694342	-15150.985358	1.003605	0.131100	7.655257	7033,..	1.593152
128	suppressed	0.694338	-16536.467718	1.000250	0.227072	4.404989	2682,..	1.298022
185	suppressed	0.694315	-13738.270855	1.009902	0.164943	6.122722	4432,..	1.465954
173	suppressed	0.694313	-15883.089098	1.010970	0.174569	5.791239	4027,..	1.474913
49	suppressed	0.694281	-17244.301344	0.992480	0.222056	4.469505	2967,..	1.288843
197	suppressed	0.694280	-13643.979833	1.000934	0.194265	5.152403	2254,..	1.498778
87	suppressed	0.694267	-16629.203447	0.995603	0.191165	5.208070	2656,..	1.417589
171	suppressed	0.694237	-14655.811006	1.003676	0.138184	7.263338	7563,..	1.574202
134	suppressed	0.694172	-8769.493202	1.000198	0.090513	11.050361	14864,..	1.768039
6	suppressed	0.694171	-15555.987352	0.994548	0.158038	6.293098	5609,..	1.512444
107	suppressed	0.693943	-14159.521215	1.008739	0.152237	6.626090	3627,..	1.475668
98	suppressed	0.693925	-16305.551122	1.003250	0.175467	5.717609	5049,..	1.439092
19	suppressed	0.693917	-13874.488687	1.017081	0.181240	5.611794	4695,..	1.447960
71	suppressed	0.693914	-16364.352404	0.996756	0.171728	5.804261	5454,..	1.415694
137	suppressed	0.693901	-15117.836331	0.989319	0.156571	6.318654	2208,..	1.636275
92	suppressed	0.693895	-16730.019051	1.005339	0.200997	5.001760	3714,..	1.329585
151	suppressed	0.693877	-16347.669237	1.001342	0.167465	5.979419	5545,..	1.433163
177	suppressed	0.693819	-16232.167039	1.000425	0.232371	4.305291	1694,..	1.396094
76	suppressed	0.693783	-14796.647482	0.990767	0.143743	6.892647	1966,..	1.729334
169	suppressed	0.693747	-14755.260130	0.996178	0.135208	7.367745	3340,..	1.640316
41	suppressed	0.693740	-17832.648444	1.000571	0.273132	3.663323	2284,..	1.174987
29	suppressed	0.693730	-16794.469280	1.006752	0.190572	5.282797	4328,..	1.349449
174	suppressed	0.693650	-16425.707625	1.000743	0.190659	5.248862	2101,..	1.450030
73	suppressed	0.693591	-15905.953102	0.997153	0.195337	5.104782	1864,..	1.526860
22	suppressed	0.693492	-16180.359940	0.999066	0.145244	6.878535	4600,..	1.491685
37	suppressed	0.693463	-12675.489582	1.004201	0.131172	7.655599	1798,..	1.885736
51	suppressed	0.693445	-15151.229348	0.994113	0.132859	7.482468	6056,..	1.614267
63	suppressed	0.693369	-15653.765872	0.999983	0.153297	6.523169	2486,..	1.631502
146	suppressed	0.693074	-16487.993317	1.017384	0.179676	5.662324	6159,..	1.396452
50	suppressed	0.693036	-14052.624639	0.970005	0.117852	8.230672	6736,..	1.675559
20	suppressed	0.693028	-12364.355925	1.003365	0.115630	8.677385	3367,..	1.765088
194	suppressed	0.692945	-15805.385445	0.989678	0.149223	6.632210	6822,..	1.489779
80	suppressed	0.692882	-14273.457012	1.004135	0.139847	7.180234	10160,..	1.578089
116	suppressed	0.692686	-18097.210380	1.016817	0.285228	3.564926	2972,..	1.087331
132	suppressed	0.692673	-18432.020836	0.989300	0.318265	3.108419	1567,..	1.016520
180	suppressed	0.692585	-16033.236868	1.001339	0.156398	6.402497	1619,..	1.558806
15	suppressed	0.692562	-18077.831238	0.999079	0.290434	3.439956	1318,..	1.106852
9	suppressed	0.692526	-14921.955589	1.000281	0.090554	11.046244	4794,..	1.701320
167	suppressed	0.692142	-13294.262711	1.001934	0.249682	4.012837	927,..	1.347249
65	suppressed	0.692015	-15158.321299	0.999915	0.268486	3.724273	2478,..	1.166006
40	suppressed	0.691969	-14626.548881	0.998482	0.177429	5.627515	999,..	1.559129
85	suppressed	0.691758	-12029.802566	0.987214	0.056552	17.456793	5942,..	1.867132
145	suppressed	0.691695	-14977.745283	0.998701	0.092298	10.820350	4117,..	1.700267
121	suppressed	0.691649	-16852.495659	1.002502	0.198143	5.059479	1279,..	1.448922
109	suppressed	0.691624	11.105179	0.990409	0.092495	10.707753	-13237,..	2.294190
42	suppressed	0.691413	-14119.200441	0.999907	0.162818	6.141246	1923,..	1.683997
102	suppressed	0.691146	-15496.540162	1.004959	0.155285	6.471688	6389,..	1.556367
95	suppressed	0.690974	-13939.120994	0.999838	0.264063	3.786364	-6590,..	1.228277
88	suppressed	0.690820	-14844.309111	1.014893	0.167929	6.043600	2945,..	1.416806
188	suppressed	0.690502	-12473.763646	0.994681	0.161716	6.150772	480,..	1.511505
4	suppressed	0.690244	-15635.011527	1.001321	0.339025	2.953533	808,..	1.074127
182	suppressed	0.689944	-14860.967741	1.017382	0.367334	2.769637	979,..	1.026860
136	suppressed	0.689725	-15719.484869	1.009689	0.213316	4.733294	1237,..	1.362489
108	suppressed	0.689267	-16868.286217	1.010455	0.398269	2.537115	1003,..	0.951770
179	suppressed	0.688211	-14695.841635	1.002931	0.144639	6.934023	3931,..	1.532106
78	suppressed	0.688161	-14766.394484	0.995935	0.285833	3.484327	269,..	1.178970
58	suppressed	0.688110	-14520.763303	0.993134	0.221677	4.480086	-57,..	1.317097
158	suppressed	0.687926	-12973.184141	1.006087	0.053497	18.806378	6828,..	2.049839
69	suppressed	0.687829	-15418.965515	0.993655	0.194727	5.102813	67,..	1.446613
97	suppressed	0.687648	-14316.538926	0.998669	0.093484	10.682791	334,..	1.750742
101	suppressed	0.687579	-15265.916851	1.000543	0.210953	4.742970	54,..	1.394806
144	suppressed	0.687376	-17053.219866	0.996680	0.255810	3.896177	267,..	1.238990
199	suppressed	0.687220	-11473.991534	0.986581	0.147360	6.695019	-67,..	1.615409
150	suppressed	0.687185	-18000.814671	0.999958	0.310344	3.222093	99,..	1.064935
119	suppressed	0.687071	-15847.464550	1.018856	0.171281	5.948439	4099,..	1.444477
104	suppressed	0.686985	-17326.854985	1.002006	0.357138	2.805658	456,..	1.042707
159	suppressed	0.686388	-12400.171387	0.968001	0.177878	5.441929	-209,..	1.478802
61	suppressed	0.686180	-18478.487653	1.000314	0.323603	3.091179	6,..	0.965752
91	suppressed	0.685937	-16890.233842	0.990974	0.403501	2.455938	945,..	0.877447
39	suppressed	0.685844	-12491.236604	0.992076	0.189296	5.240883	14,..	1.431698
84	suppressed	0.685798	-16772.993369	1.001093	0.164108	6.100214	533,..	1.397848
164	suppressed	0.685721	-15322.547333	1.000197	0.268926	3.719232	-64,..	1.206657
191	suppressed	0.685667	-13083.237456	0.997780	0.051077	19.534712	9402,..	1.822835
34	suppressed	0.684785	-19254.480430	1.010454	0.423364	2.386724	927,..	0.801820
186	suppressed	0.684724	-14336.818556	0.995270	0.065934	15.094839	4920,..	1.799114
125	suppressed	0.684648	-7343.474624	1.003233	0.003889	257.959484	13378,..	2.425804
195	suppressed	0.684487	-15088.262603	1.010624	0.081418	12.412841	4535,..	1.707507
44	suppressed	0.684426	-12602.203000	0.992964	0.118584	8.373496	2830,..	1.587170
57	suppressed	0.684315	-15432.307927	0.987260	0.103845	9.507039	3405,..	1.599209
35	suppressed	0.684293	-15977.929676	1.005304	0.146402	6.866719	3560,..	1.468133
90	suppressed	0.683921	-10368.230592	1.006699	0.020568	48.945316	9070,..	2.053029
8	suppressed	0.683656	-14324.525128	1.003344	0.067062	14.961539	3683,..	1.755301
143	suppressed	0.683650	-13322.100404	1.004908	0.044540	22.561888	4260,..	1.889706
149	suppressed	0.683370	-9704.420410	0.989848	0.040784	24.270205	3023,..	1.967927
13	suppressed	0.683208	-12418.264857	1.002041	0.032487	30.844432	3250,..	1.941987
81	suppressed	0.683157	-8065.420824	0.997320	0.026629	37.452357	5560,..	2.111768
155	suppressed	0.683082	-5851.033242	1.003461	0.015278	65.681385	4198,..	2.211873
162	suppressed	0.683080	-13541.997285	1.003286	0.067781	14.801812	2761,..	1.737107
118	suppressed	0.682673	-12636.113605	0.996464	0.097281	10.243117	784,..	1.652231
129	suppressed	0.682144	-15346.049849	1.001480	0.170969	5.857670	1160,..	1.378440
10	suppressed	0.682071	-15331.747861	0.999931	0.149585	6.684724	823,..	1.418027
94	suppressed	0.681737	-9516.486317	0.999176	0.029912	33.403877	2670,..	2.135083
126	suppressed	0.681299	-16683.012731	1.014760	0.192281	5.277496	1351,..	1.308661
47	suppressed	0.681072	-11282.463532	0.985347	0.102623	9.601607	1039,..	1.626720
1	suppressed	0.680545	-14839.037213	1.003692	0.083693	11.992469	829,..	1.717836
31	suppressed	0.680447	-14652.873022	1.015388	0.171427	5.923161	1006,..	1.402128
110	suppressed	0.680445	-10737.317516	0.996909	0.038054	26.197516	1706,..	1.984588
103	suppressed	0.680326	-9534.988898	1.027888	0.061813	16.629039	2996,..	1.967403
198	suppressed	0.680190	-14300.365282	1.009282	0.350376	2.880571	-7534,..	1.057658
64	suppressed	0.680186	-13690.852850	0.974986	0.254555	3.830155	1094,..	1.124125
172	suppressed	0.679898	-12051.276254	0.978729	0.162753	6.013589	456,..	1.591660
156	suppressed	0.679765	-9414.953722	1.018092	0.040578	25.089760	809,..	2.035834
86	suppressed	0.678314	-18485.870253	1.008287	0.315098	3.199922	425,..	0.955099
166	suppressed	0.674830	-10213.109846	1.006027	0.009063	111.007384	589,..	2.314159
17	suppressed	0.674305	-7399.358588	0.994818	0.006210	160.208548	-26,..	2.443177
33	suppressed	0.674213	-11999.325020	0.997773	0.026210	38.067764	947,..	2.166395
153	suppressed	0.667498	-10779.112703	0.995397	0.125103	7.956619	3265,..	1.909201
160	suppressed	0.666145	-12184.310797	0.953825	0.092485	10.313298	1992,..	1.802685
154	suppressed	0.660428	-11935.061554	1.001120	0.144910	6.908575	4217,..	1.810810
161	suppressed	0.658164	-9106.352917	1.010160	0.048330	20.901503	-8894,..	2.054313
122	suppressed	0.658141	-12382.678106	1.008789	0.070623	14.284063	-6513,..	1.926606
165	suppressed	0.657027	-10913.849168	1.014197	0.064141	15.811935	-7706,..	1.997895
192	suppressed	0.641364	-3971.288308	0.993242	0.033069	30.035167	4877,..	2.237789
189	suppressed	0.637593	-17525.524191	1.015197	0.222545	4.561765	1681,..	1.244728
30	suppressed	0.614605	-1530.266321	0.993504	0.000391	2542.547937	1741,..	2.684948
82	suppressed	0.541329	870.648161	1.005770	0.000019	53113.958037	5372,..	3.934987
99	suppressed	0.507435	-6071.440214	1.026697	0.027348	37.541558	4211,..	3.066611
141	suppressed	0.488867	-1285.003906	0.931659	0.009375	99.375559	845,..	2.713847
55	suppressed	0.474265	-12607.693930	1.177187	0.005371	219.186429	8453,..	1.726284
196	suppressed	0.299159	-8098.683107	0.946400	0.012435	76.108961	4247,..	2.373405

	model	r	G0	max binding	min binding	ratio	energies	information	logo	PCA1	PCA2
56	findmotif(both_strands=False, fit_G0=True, mot...	0.706996	-9177.028204	0.972033	0.133311	7.291495	[-7053.185480104829, 14371.734708221606, -8323...	2.362101	<PIL.Image.Image image mode=RGB size=1200x900 ...	19825.322932	-1553.438968
142	findmotif(both_strands=False, fit_G0=True, mot...	0.706961	-10863.349327	0.964528	0.124707	7.734337	[-5334.0554370904765, 9281.749185392397, -6488...	2.310951	<PIL.Image.Image image mode=RGB size=1200x900 ...	16781.517546	-769.408837
157	findmotif(both_strands=False, fit_G0=True, mot...	0.706862	-9727.766114	0.966156	0.135750	7.117146	[-6577.773398411619, 13912.727582882128, -7976...	2.317627	<PIL.Image.Image image mode=RGB size=1200x900 ...	19146.576793	-1523.318826
148	findmotif(both_strands=False, fit_G0=True, mot...	0.706826	-11675.698748	0.967140	0.134469	7.192312	[-4909.541315277625, 8409.74004047994, -6169.3...	2.269945	<PIL.Image.Image image mode=RGB size=1200x900 ...	15561.083980	-897.311099
163	findmotif(both_strands=False, fit_G0=True, mot...	0.706807	-11486.910299	0.965590	0.136809	7.057952	[-5785.075296653869, 11148.827507828752, -6951...	2.214978	<PIL.Image.Image image mode=RGB size=1200x900 ...	16311.527973	-1288.988453
...	...	...	...	...	...	...	...	...	...	...	...
110	findmotif(both_strands=False, fit_G0=True, mot...	0.680445	-10737.317516	0.996909	0.038054	26.197516	[1706.4767801559324, -4603.711766799798, 7958....	1.984588	<PIL.Image.Image image mode=RGB size=1200x900 ...	-17787.806490	-1832.951702
103	findmotif(both_strands=False, fit_G0=True, mot...	0.680326	-9534.988898	1.027888	0.061813	16.629039	[2996.130586825946, -2864.2646122122032, 2348....	1.967403	<PIL.Image.Image image mode=RGB size=1200x900 ...	-18716.013085	-6072.519659
198	findmotif(both_strands=False, fit_G0=True, mot...	0.680190	-14300.365282	1.009282	0.350376	2.880571	[-7534.602343903572, 17518.12083155398, -6352....	1.057658	<PIL.Image.Image image mode=RGB size=1200x900 ...	14420.431194	-580.803961
64	findmotif(both_strands=False, fit_G0=True, mot...	0.680186	-13690.852850	0.974986	0.254555	3.830155	[1094.2766885463207, -722.0191909075406, 474.3...	1.124125	<PIL.Image.Image image mode=RGB size=1200x900 ...	-2341.691988	19657.579257
172	findmotif(both_strands=False, fit_G0=True, mot...	0.679898	-12051.276254	0.978729	0.162753	6.013589	[456.14039991784085, 20.44544959578485, 0.7665...	1.591660	<PIL.Image.Image image mode=RGB size=1200x900 ...	3391.185915	-12443.880252

	model	r	G0	max binding	min binding	ratio	energies	information	PCA1	PCA2
56	suppressed	0.706996	-9177.028204	0.972033	0.133311	7.291495	-7053,..	2.362101	19825.322932	-1553.438968
142	suppressed	0.706961	-10863.349327	0.964528	0.124707	7.734337	-5334,..	2.310951	16781.517546	-769.408837
157	suppressed	0.706862	-9727.766114	0.966156	0.135750	7.117146	-6577,..	2.317627	19146.576793	-1523.318826
148	suppressed	0.706826	-11675.698748	0.967140	0.134469	7.192312	-4909,..	2.269945	15561.083980	-897.311099
163	suppressed	0.706807	-11486.910299	0.965590	0.136809	7.057952	-5785,..	2.214978	16311.527973	-1288.988453
131	suppressed	0.706774	-9728.752465	0.979635	0.121770	8.044950	-5290,..	2.300738	18368.688916	-648.153351
106	suppressed	0.706773	-7915.539991	0.952564	0.124481	7.652290	-8470,..	2.364914	21767.273901	-1973.738421
147	suppressed	0.706685	-10933.515501	0.965363	0.132006	7.313014	-4346,..	2.340699	16241.576656	-51.849408
70	suppressed	0.706631	-12214.012249	0.963979	0.139102	6.930042	-5148,..	2.223107	14771.727280	-1215.450068
89	suppressed	0.706580	-11248.116017	0.968648	0.136544	7.094042	-6221,..	2.123406	17131.145911	-1241.442253
170	suppressed	0.706539	-12052.588484	0.963785	0.144152	6.685888	-4561,..	2.256433	14838.411635	-994.248033
113	suppressed	0.706530	-11913.812017	0.969498	0.126538	7.661721	-4487,..	2.247607	15168.678487	-310.031692
52	suppressed	0.706515	-12926.542010	0.965676	0.140475	6.874351	-4724,..	2.109999	13999.504943	-816.220411
2	suppressed	0.706428	-12557.874320	0.958903	0.147678	6.493219	-4832,..	2.246795	14139.117443	-1242.466310
193	suppressed	0.706419	-13273.206683	0.974611	0.142844	6.822906	-4308,..	2.144290	13243.097834	-701.941397
67	suppressed	0.706398	-12923.995193	0.967148	0.137279	7.045115	-4610,..	2.111763	14050.827382	-776.543545
115	suppressed	0.706326	-12983.544326	0.972911	0.146713	6.631377	-5042,..	2.079642	14094.253400	-1114.332846
59	suppressed	0.706294	-12176.505872	0.965304	0.148764	6.488806	-5943,..	2.064881	15677.738878	-1563.947147
0	suppressed	0.706252	-12343.309172	0.956334	0.143811	6.649934	-5709,..	2.072961	15282.208651	-1500.888004
117	suppressed	0.706111	-12177.537911	0.970942	0.134118	7.239444	-3974,..	2.095936	14931.934693	429.135675
18	suppressed	0.706074	-13749.828576	0.975109	0.156092	6.246997	-4223,..	2.058359	12819.950581	-817.226814
127	suppressed	0.706036	-10854.233141	0.953125	0.141681	6.727261	-6960,..	2.116820	17936.973959	-2214.055464
79	suppressed	0.705990	-12794.544096	0.974744	0.141340	6.896470	-4728,..	2.046540	14346.213502	-439.569626
27	suppressed	0.705884	-10317.128912	0.967302	0.150407	6.431250	-4349,..	2.238211	16939.020766	-681.852972
77	suppressed	0.705877	-14307.909056	0.969338	0.152928	6.338521	-4033,..	1.948135	11968.350919	-626.428754
5	suppressed	0.705833	-13465.399731	0.968937	0.154969	6.252474	-4878,..	1.966427	13548.939020	-933.322459
28	suppressed	0.705824	-13257.450651	0.970967	0.151509	6.408643	-5022,..	2.031888	13742.292223	-1352.316119
12	suppressed	0.705811	-11814.471246	0.965943	0.154481	6.252820	-6645,..	2.010246	16481.500242	-2044.963493
190	suppressed	0.705788	-14142.449874	0.973330	0.155344	6.265625	-4195,..	2.043055	11921.744929	-959.975450
130	suppressed	0.705769	-13728.165226	0.967874	0.140905	6.868995	-4015,..	2.022657	12664.142830	-352.722105
105	suppressed	0.705719	-14379.041850	0.978379	0.154051	6.350986	-4013,..	1.993526	11651.704367	-926.449523
111	suppressed	0.705703	-12947.902083	0.970576	0.149884	6.475512	-4823,..	1.953821	14488.182566	-550.496706
135	suppressed	0.705640	-14039.619117	0.973976	0.150254	6.482181	-3802,..	2.004695	12390.326511	-304.126450
43	suppressed	0.705622	-14487.299010	0.967699	0.160324	6.035891	-3927,..	1.978947	11565.797799	-943.929132
83	suppressed	0.705541	-14577.425748	0.981237	0.163550	5.999600	-4077,..	1.946875	11633.274617	-989.040420
178	suppressed	0.705485	-14061.491830	0.968489	0.159125	6.086355	-3858,..	1.959808	12302.571146	-253.736307
32	suppressed	0.705459	-14266.668919	0.967770	0.170622	5.672020	-4150,..	2.023246	11857.043778	-1355.668409
26	suppressed	0.705413	-14701.042064	0.978571	0.169238	5.782209	-3986,..	1.912349	11466.745134	-733.490110
54	suppressed	0.705339	-14959.846587	0.975378	0.162206	6.013202	-3678,..	1.902153	10845.393256	-666.955906
45	suppressed	0.705324	-13361.090070	0.968532	0.159637	6.067098	-4836,..	2.026302	13407.079649	-1470.679646
114	suppressed	0.705315	-12670.666987	0.976832	0.158935	6.146115	-6041,..	1.986389	15070.371758	-2014.172460
24	suppressed	0.705314	-14054.154743	0.980018	0.155556	6.300092	-4528,..	1.938419	12638.081816	-997.882414
14	suppressed	0.705282	-14088.876587	0.974698	0.152134	6.406837	-3837,..	2.043591	11932.788908	-1056.594018
21	suppressed	0.705188	-8305.931026	0.962993	0.144902	6.645806	-8188,..	2.340227	21291.674323	-1969.240440
48	suppressed	0.705135	-14723.661981	0.972560	0.172590	5.635099	-3644,..	1.984678	11012.099537	-1135.085789
36	suppressed	0.705020	-14820.934033	0.977018	0.168904	5.784454	-3864,..	1.823897	11427.236652	-409.257273
120	suppressed	0.704992	-14451.163255	0.978946	0.175247	5.586075	-4413,..	1.908539	11918.222660	-1354.407693
140	suppressed	0.704955	-12370.971274	0.974654	0.156738	6.218381	-6621,..	1.886788	15890.019253	-1998.100752
124	suppressed	0.704954	-11465.242231	0.967891	0.169187	5.720832	-7332,..	1.946722	17430.891653	-2504.510853
139	suppressed	0.704909	-13699.534998	0.972513	0.166307	5.847711	-4736,..	2.000360	12896.912172	-1539.780510
100	suppressed	0.704803	-15229.762285	0.991523	0.166159	5.967320	-3816,..	1.799674	10755.995452	-589.810876
187	suppressed	0.704578	-14778.630303	0.961413	0.175678	5.472591	-4233,..	1.857307	11279.798987	-1160.039032
181	suppressed	0.704422	-15758.529236	0.994799	0.192793	5.159941	-3386,..	1.755134	10047.228292	-689.029558
3	suppressed	0.704033	-14230.229409	0.971167	0.173523	5.596750	-3634,..	1.964398	11583.092292	-1240.207485
7	suppressed	0.704010	-13187.195178	0.972048	0.172629	5.630850	-5467,..	2.011791	13803.072026	-2213.981689
25	suppressed	0.703962	-15155.136559	0.974104	0.186596	5.220385	-3974,..	1.817961	10780.247655	-1283.696949
60	suppressed	0.703665	-15048.014390	1.002399	0.193495	5.180495	-4302,..	1.708500	11684.422506	-767.178284
46	suppressed	0.703537	-14556.597842	0.979293	0.175544	5.578615	-4895,..	1.762471	12243.393601	-1571.594710
16	suppressed	0.703486	-15225.570281	0.971553	0.173898	5.586916	-3398,..	1.814056	10347.076762	-929.536865
123	suppressed	0.703194	-16545.540324	0.981143	0.209328	4.687109	-3038,..	1.554067	8884.689560	-406.982919
72	suppressed	0.703165	-15850.112081	0.973095	0.203243	4.787836	-3353,..	1.773192	9331.497704	-1317.958182
75	suppressed	0.698884	-18233.733094	0.996786	0.311778	3.197099	-2342,..	1.082989	6792.692662	194.093183
38	suppressed	0.695414	-13623.049624	0.994722	0.202557	4.910824	-3831,..	2.396506	8711.652512	-5313.586987
138	suppressed	0.694687	-15089.902655	1.014728	0.220736	4.597027	-3338,..	2.119809	7852.274101	-4247.636299
23	suppressed	0.694529	-16078.835412	1.013021	0.181807	5.571966	3802,..	1.433959	-10023.210293	-637.569871
152	suppressed	0.694514	-15316.049402	0.999945	0.148745	6.722527	4993,..	1.550912	-11111.614743	-468.139196
11	suppressed	0.694501	-16062.485560	0.998670	0.169212	5.901884	3614,..	1.456914	-9724.916594	-835.648705
112	suppressed	0.694494	-13742.600462	0.997189	0.111924	8.909516	6957,..	1.689719	-13587.249131	-837.856520
175	suppressed	0.694472	-14164.875513	0.996417	0.138673	7.185389	5902,..	1.575634	-13248.641898	-1130.557415
66	suppressed	0.694472	-15002.774532	1.008638	0.126429	7.977893	5820,..	1.604546	-11564.776019	-506.606129
168	suppressed	0.694467	-13724.735721	1.004490	0.133346	7.532940	6287,..	1.566653	-14218.684203	-1415.891101
184	suppressed	0.694458	-15387.275370	0.994654	0.188431	5.278623	2995,..	1.401132	-11123.177265	-1788.048732
133	suppressed	0.694448	-12414.691250	0.994884	0.109906	9.052176	9312,..	1.711392	-15688.551544	-1395.469676
93	suppressed	0.694435	-14169.410972	1.002387	0.124961	8.021589	9148,..	1.639763	-12731.429311	16.455778
62	suppressed	0.694433	-12264.565203	1.005852	0.122552	8.207558	9222,..	1.686218	-16241.115544	-1299.554446
183	suppressed	0.694432	-14371.123342	0.994981	0.193133	5.151792	3347,..	1.392146	-13286.716322	-2216.436068
53	suppressed	0.694412	-15834.883136	1.005492	0.156837	6.411059	4776,..	1.484806	-10460.244360	-203.317795
96	suppressed	0.694405	-15150.371077	1.000244	0.148052	6.756014	3849,..	1.513579	-11384.948211	-1342.632396
68	suppressed	0.694395	-13458.785667	0.997705	0.120960	8.248227	8730,..	1.687686	-14068.475141	-372.668187
176	suppressed	0.694343	-14975.228364	1.005385	0.155619	6.460571	3759,..	1.504388	-11672.541902	-1730.282171
74	suppressed	0.694342	-15150.985358	1.003605	0.131100	7.655257	7033,..	1.593152	-11181.438381	82.700783
128	suppressed	0.694338	-16536.467718	1.000250	0.227072	4.404989	2682,..	1.298022	-9320.232085	-954.802551
185	suppressed	0.694315	-13738.270855	1.009902	0.164943	6.122722	4432,..	1.465954	-14439.167424	-2029.086214
173	suppressed	0.694313	-15883.089098	1.010970	0.174569	5.791239	4027,..	1.474913	-10251.825983	-556.507463
49	suppressed	0.694281	-17244.301344	0.992480	0.222056	4.469505	2967,..	1.288843	-7734.327905	-274.185714
197	suppressed	0.694280	-13643.979833	1.000934	0.194265	5.152403	2254,..	1.498778	-13408.452129	-4116.620574
87	suppressed	0.694267	-16629.203447	0.995603	0.191165	5.208070	2656,..	1.417589	-8220.411749	-1115.364132
171	suppressed	0.694237	-14655.811006	1.003676	0.138184	7.263338	7563,..	1.574202	-12387.416218	-72.885598
134	suppressed	0.694172	-8769.493202	1.000198	0.090513	11.050361	14864,..	1.768039	-21542.687741	-2743.714968
6	suppressed	0.694171	-15555.987352	0.994548	0.158038	6.293098	5609,..	1.512444	-10892.025742	25.127517
107	suppressed	0.693943	-14159.521215	1.008739	0.152237	6.626090	3627,..	1.475668	-13374.189378	-2417.045582
98	suppressed	0.693925	-16305.551122	1.003250	0.175467	5.717609	5049,..	1.439092	-9610.628337	425.002572
19	suppressed	0.693917	-13874.488687	1.017081	0.181240	5.611794	4695,..	1.447960	-14482.376802	-1655.864059
71	suppressed	0.693914	-16364.352404	0.996756	0.171728	5.804261	5454,..	1.415694	-9540.638665	400.032835
137	suppressed	0.693901	-15117.836331	0.989319	0.156571	6.318654	2208,..	1.636275	-9712.776640	-3152.350007
92	suppressed	0.693895	-16730.019051	1.005339	0.200997	5.001760	3714,..	1.329585	-9253.567844	53.840156
151	suppressed	0.693877	-16347.669237	1.001342	0.167465	5.979419	5545,..	1.433163	-9411.976001	345.875164
177	suppressed	0.693819	-16232.167039	1.000425	0.232371	4.305291	1694,..	1.396094	-8720.478226	-2582.689007
76	suppressed	0.693783	-14796.647482	0.990767	0.143743	6.892647	1966,..	1.729334	-9296.541519	-4186.106620
169	suppressed	0.693747	-14755.260130	0.996178	0.135208	7.367745	3340,..	1.640316	-11050.095772	-2165.412654
41	suppressed	0.693740	-17832.648444	1.000571	0.273132	3.663323	2284,..	1.174987	-6705.237699	-690.279919
29	suppressed	0.693730	-16794.469280	1.006752	0.190572	5.282797	4328,..	1.349449	-9075.294297	259.033757
174	suppressed	0.693650	-16425.707625	1.000743	0.190659	5.248862	2101,..	1.450030	-8047.013677	-2143.823229
73	suppressed	0.693591	-15905.953102	0.997153	0.195337	5.104782	1864,..	1.526860	-8521.780361	-2896.593651
22	suppressed	0.693492	-16180.359940	0.999066	0.145244	6.878535	4600,..	1.491685	-9230.592814	-54.956971
37	suppressed	0.693463	-12675.489582	1.004201	0.131172	7.655599	1798,..	1.885736	-10392.263597	-7123.273765
51	suppressed	0.693445	-15151.229348	0.994113	0.132859	7.482468	6056,..	1.614267	-11064.439221	-189.300936
63	suppressed	0.693369	-15653.765872	0.999983	0.153297	6.523169	2486,..	1.631502	-8682.278627	-2330.610230
146	suppressed	0.693074	-16487.993317	1.017384	0.179676	5.662324	6159,..	1.396452	-9417.684588	834.733301
50	suppressed	0.693036	-14052.624639	0.970005	0.117852	8.230672	6736,..	1.675559	-13023.202721	16.495359
20	suppressed	0.693028	-12364.355925	1.003365	0.115630	8.677385	3367,..	1.765088	-14711.046014	-4489.799946
194	suppressed	0.692945	-15805.385445	0.989678	0.149223	6.632210	6822,..	1.489779	-9956.787115	256.602057
80	suppressed	0.692882	-14273.457012	1.004135	0.139847	7.180234	10160,..	1.578089	-12072.029588	1076.776803
116	suppressed	0.692686	-18097.210380	1.016817	0.285228	3.564926	2972,..	1.087331	-7160.449564	593.454611
132	suppressed	0.692673	-18432.020836	0.989300	0.318265	3.108419	1567,..	1.016520	-5534.968765	-807.381211
180	suppressed	0.692585	-16033.236868	1.001339	0.156398	6.402497	1619,..	1.558806	-7120.521525	-3753.549888
15	suppressed	0.692562	-18077.831238	0.999079	0.290434	3.439956	1318,..	1.106852	-5624.860606	-1572.510799
9	suppressed	0.692526	-14921.955589	1.000281	0.090554	11.046244	4794,..	1.701320	-10596.164661	-1395.522557
167	suppressed	0.692142	-13294.262711	1.001934	0.249682	4.012837	927,..	1.347249	3253.720938	18514.245775
65	suppressed	0.692015	-15158.321299	0.999915	0.268486	3.724273	2478,..	1.166006	-12583.383401	-1746.586390
40	suppressed	0.691969	-14626.548881	0.998482	0.177429	5.627515	999,..	1.559129	-8725.830443	-5958.660394
85	suppressed	0.691758	-12029.802566	0.987214	0.056552	17.456793	5942,..	1.867132	-15302.839376	-3199.799481
145	suppressed	0.691695	-14977.745283	0.998701	0.092298	10.820350	4117,..	1.700267	-10412.430478	-1534.004474
121	suppressed	0.691649	-16852.495659	1.002502	0.198143	5.059479	1279,..	1.448922	-5766.831824	-3153.164930
109	suppressed	0.691624	11.105179	0.990409	0.092495	10.707753	-13237,..	2.294190	32030.184953	742.500663
42	suppressed	0.691413	-14119.200441	0.999907	0.162818	6.141246	1923,..	1.683997	-10660.683793	-4411.154839
102	suppressed	0.691146	-15496.540162	1.004959	0.155285	6.471688	6389,..	1.556367	-9758.924938	283.301817
95	suppressed	0.690974	-13939.120994	0.999838	0.264063	3.786364	-6590,..	1.228277	14836.846520	-360.605470
88	suppressed	0.690820	-14844.309111	1.014893	0.167929	6.043600	2945,..	1.416806	-12693.119033	-1712.761478
188	suppressed	0.690502	-12473.763646	0.994681	0.161716	6.150772	480,..	1.511505	3134.442980	19726.771878
4	suppressed	0.690244	-15635.011527	1.001321	0.339025	2.953533	808,..	1.074127	997.594195	15398.035053
182	suppressed	0.689944	-14860.967741	1.017382	0.367334	2.769637	979,..	1.026860	373.566810	17919.754793
136	suppressed	0.689725	-15719.484869	1.009689	0.213316	4.733294	1237,..	1.362489	1993.645529	13282.476210
108	suppressed	0.689267	-16868.286217	1.010455	0.398269	2.537115	1003,..	0.951770	500.213960	13192.986270
179	suppressed	0.688211	-14695.841635	1.002931	0.144639	6.934023	3931,..	1.532106	-12959.688972	-216.249924
78	suppressed	0.688161	-14766.394484	0.995935	0.285833	3.484327	269,..	1.178970	2963.916969	-9866.555736
58	suppressed	0.688110	-14520.763303	0.993134	0.221677	4.480086	-57,..	1.317097	3121.352760	-10271.443008
158	suppressed	0.687926	-12973.184141	1.006087	0.053497	18.806378	6828,..	2.049839	-11560.467523	-3524.108415
69	suppressed	0.687829	-15418.965515	0.993655	0.194727	5.102813	67,..	1.446613	2510.251568	-9048.627678
97	suppressed	0.687648	-14316.538926	0.998669	0.093484	10.682791	334,..	1.750742	5900.149692	11360.419684
101	suppressed	0.687579	-15265.916851	1.000543	0.210953	4.742970	54,..	1.394806	2837.518386	-9249.189096
144	suppressed	0.687376	-17053.219866	0.996680	0.255810	3.896177	267,..	1.238990	2153.952743	-6639.572600
199	suppressed	0.687220	-11473.991534	0.986581	0.147360	6.695019	-67,..	1.615409	3719.498433	-14477.496567
150	suppressed	0.687185	-18000.814671	0.999958	0.310344	3.222093	99,..	1.064935	1899.399844	-5308.274796
119	suppressed	0.687071	-15847.464550	1.018856	0.171281	5.948439	4099,..	1.444477	-11383.878598	737.467934
104	suppressed	0.686985	-17326.854985	1.002006	0.357138	2.805658	456,..	1.042707	2365.938768	-6175.180399
159	suppressed	0.686388	-12400.171387	0.968001	0.177878	5.441929	-209,..	1.478802	4078.192048	-12958.317469
61	suppressed	0.686180	-18478.487653	1.000314	0.323603	3.091179	6,..	0.965752	1754.121881	-4706.724191
91	suppressed	0.685937	-16890.233842	0.990974	0.403501	2.455938	945,..	0.877447	-500.576252	13477.920046
39	suppressed	0.685844	-12491.236604	0.992076	0.189296	5.240883	14,..	1.431698	4059.983855	-12945.296033
84	suppressed	0.685798	-16772.993369	1.001093	0.164108	6.100214	533,..	1.397848	4062.851661	8565.301443
164	suppressed	0.685721	-15322.547333	1.000197	0.268926	3.719232	-64,..	1.206657	2862.452431	-9067.590347
191	suppressed	0.685667	-13083.237456	0.997780	0.051077	19.534712	9402,..	1.822835	-14663.426470	925.053849
34	suppressed	0.684785	-19254.480430	1.010454	0.423364	2.386724	927,..	0.801820	2243.350482	6157.224735
186	suppressed	0.684724	-14336.818556	0.995270	0.065934	15.094839	4920,..	1.799114	-12694.830772	949.860274
125	suppressed	0.684648	-7343.474624	1.003233	0.003889	257.959484	13378,..	2.425804	-16410.723754	-8085.906606
195	suppressed	0.684487	-15088.262603	1.010624	0.081418	12.412841	4535,..	1.707507	-11767.027918	1212.078376
44	suppressed	0.684426	-12602.203000	0.992964	0.118584	8.373496	2830,..	1.587170	-16689.161603	-1470.724165
57	suppressed	0.684315	-15432.307927	0.987260	0.103845	9.507039	3405,..	1.599209	-11344.106114	1090.086289
35	suppressed	0.684293	-15977.929676	1.005304	0.146402	6.866719	3560,..	1.468133	-10904.424687	1216.334643
90	suppressed	0.683921	-10368.230592	1.006699	0.020568	48.945316	9070,..	2.053029	-18137.990629	1116.859320
8	suppressed	0.683656	-14324.525128	1.003344	0.067062	14.961539	3683,..	1.755301	-12820.389101	1245.937888
143	suppressed	0.683650	-13322.100404	1.004908	0.044540	22.561888	4260,..	1.889706	-13977.907723	1415.837901
149	suppressed	0.683370	-9704.420410	0.989848	0.040784	24.270205	3023,..	1.967927	-20216.342673	-1731.741537
13	suppressed	0.683208	-12418.264857	1.002041	0.032487	30.844432	3250,..	1.941987	-14761.860714	1700.616363
81	suppressed	0.683157	-8065.420824	0.997320	0.026629	37.452357	5560,..	2.111768	-22311.211588	-1943.523849
155	suppressed	0.683082	-5851.033242	1.003461	0.015278	65.681385	4198,..	2.211873	-23059.925071	1637.390623
162	suppressed	0.683080	-13541.997285	1.003286	0.067781	14.801812	2761,..	1.737107	-14044.565526	979.855649
118	suppressed	0.682673	-12636.113605	0.996464	0.097281	10.243117	784,..	1.652231	-1953.723729	18329.698956
129	suppressed	0.682144	-15346.049849	1.001480	0.170969	5.857670	1160,..	1.378440	-1449.402411	14096.422182
10	suppressed	0.682071	-15331.747861	0.999931	0.149585	6.684724	823,..	1.418027	-1666.154083	13136.159489
94	suppressed	0.681737	-9516.486317	0.999176	0.029912	33.403877	2670,..	2.135083	-18170.578388	779.589003
126	suppressed	0.681299	-16683.012731	1.014760	0.192281	5.277496	1351,..	1.308661	-9672.752252	1127.767688
47	suppressed	0.681072	-11282.463532	0.985347	0.102623	9.601607	1039,..	1.626720	-1918.956494	22000.990635
1	suppressed	0.680545	-14839.037213	1.003692	0.083693	11.992469	829,..	1.717836	-676.043069	10562.278890
31	suppressed	0.680447	-14652.873022	1.015388	0.171427	5.923161	1006,..	1.402128	-1204.504112	15714.993221
110	suppressed	0.680445	-10737.317516	0.996909	0.038054	26.197516	1706,..	1.984588	-17787.806490	-1832.951702
103	suppressed	0.680326	-9534.988898	1.027888	0.061813	16.629039	2996,..	1.967403	-18716.013085	-6072.519659
198	suppressed	0.680190	-14300.365282	1.009282	0.350376	2.880571	-7534,..	1.057658	14420.431194	-580.803961
64	suppressed	0.680186	-13690.852850	0.974986	0.254555	3.830155	1094,..	1.124125	-2341.691988	19657.579257
172	suppressed	0.679898	-12051.276254	0.978729	0.162753	6.013589	456,..	1.591660	3391.185915	-12443.880252
156	suppressed	0.679765	-9414.953722	1.018092	0.040578	25.089760	809,..	2.035834	-1694.158014	22864.795059
86	suppressed	0.678314	-18485.870253	1.008287	0.315098	3.199922	425,..	0.955099	-1446.853313	7834.074132
166	suppressed	0.674830	-10213.109846	1.006027	0.009063	111.007384	589,..	2.314159	1374.735976	15516.958973
17	suppressed	0.674305	-7399.358588	0.994818	0.006210	160.208548	-26,..	2.443177	6976.009462	18384.433629
33	suppressed	0.674213	-11999.325020	0.997773	0.026210	38.067764	947,..	2.166395	2392.176925	14258.187687
153	suppressed	0.667498	-10779.112703	0.995397	0.125103	7.956619	3265,..	1.909201	1601.895608	-14270.021543
160	suppressed	0.666145	-12184.310797	0.953825	0.092485	10.313298	1992,..	1.802685	1670.779356	-11301.186076
154	suppressed	0.660428	-11935.061554	1.001120	0.144910	6.908575	4217,..	1.810810	1509.728158	-11271.466543
161	suppressed	0.658164	-9106.352917	1.010160	0.048330	20.901503	-8894,..	2.054313	17810.362985	2576.397405

	stage	protein	# probes	motif length	r	AUROC	G0	G0 fitted	ratio	max binding	min binding	energies	model
0	best repetition	T7 primase	3149	4	0.706996	0.829541	-9177.028204	True	7.291495	0.972033	0.133311	-7053,..	suppressed
1	mae optimzed	T7 primase	3149	4	0.693057	0.820350	-9177.028204	True	7.291495	0.978392	0.134989	-6638,..	suppressed
2	expanded, border	T7 primase	3149	5	0.722890	0.839472	-8721.403785	True	12.464345	0.975132	0.078234	1225,..	suppressed